Local and landscape environmental heterogeneity drive ant community structure in temperate seminatural upland grasslands

Abstract Environmental heterogeneity is an important driver of ecological communities. Here, we assessed the effects of local and landscape spatial environmental heterogeneity on ant community structure in temperate seminatural upland grasslands of Central Germany. We surveyed 33 grassland sites representing a gradient in elevation and landscape composition. Local environmental heterogeneity was measured in terms of variability of temperature and moisture within and between grasslands sites. Grassland management type (pasture vs. meadows) was additionally included as a local environmental heterogeneity measure. The complexity of habitat types in the surroundings of grassland sites was used as a measure of landscape environmental heterogeneity. As descriptors of ant community structure, we considered species composition in terms of nest density, community evenness, and functional response traits. We found that extensively grazed pastures and within‐site heterogeneity in soil moisture at local scale, and a high diversity of land cover types at the landscape scale affected ant species composition by promoting higher nest densities of some species. Ant community evenness was high in wetter grasslands with low within‐site variability in soil moisture and surrounded by a less diverse landscape. Fourth‐corner models revealed that ant community structure response to environmental heterogeneity was mediated mainly by worker size, colony size, and life history traits related with colony reproduction and foundation. We discuss how within‐site local variability in soil moisture and low‐intensity grazing promote ant species densities and highlight the role of habitat temperature and humidity affecting community evenness. We hypothesize that a higher diversity of land cover types in a forest‐dominated landscape buffers less favorable environmental conditions for ant species establishment and dispersal between grasslands. We conclude that spatial environmental heterogeneity at local and landscape scale plays an important role as deterministic force in filtering ant species and, along with neutral processes (e.g., stochastic colonization), in shaping ant community structure in temperate seminatural upland grasslands.

Note: Covering the entire grassland extension for sampling was not always possible due to the presence of cattle in subsections of pastures or restricted access caused by rugged topography and flooded areas. The number of Seifert-plots established for ant survey depended on these spatial limitations and grassland size. One Seifert-plot was performed in smaller grasslands sites (< 0.77 ha), one-two plots intermediate size grasslands (0.8-1.9 ha), and two-three plots in larger sites (> 1.9 ha). On average, our ant survey procedure covered 20% of grassland sites area.
Nest density calculation. Nest abundance from each Seifert-plot component (S-, Q-, and SI-) was combined into a final integrated species-specific density (ISSD) which represents the nest density of a species within 100 m 2 (Seifert 2017). The ISSD per species is calculated as the sum of nests found in the S-, Qand SIsampling areas divided by the pseudo-area of the "recording group" (RG) to which a particular species belongs (Eq. 1; Seifert 2017). The RG is a generalization of how perceptible a nest is accordance with the ant species biology. The assignment of a species into a given RG describes the probability of finding a nest in each sampling level (S-, Q-, and SI-), and such probability is determined by nests position, type, size and density (Seifert 2017). Based on almost four decades of research on Central European ants, Seifert (2017) defined five RGs (i-v) ranking from lowest to largest perceptibility. The pseudo-area is calculated for each RG separately and provides a measure of the total intensity of investigation on a Seifert-plot per RG. This parameter may be understood as the area equivalent needed in a specific RG to find the sum of nests recorded by S-, Qand SI-search (Seifert 2017). The pseudo-area is defined as the sum of all nests detected in all search levels divided by a fixed value per RG, the recording-group-specific total density or FRSD (Eq. 2; Seifert 2017). The FRSD is the number of nests of a specific RG expected to occur in 100 m 2 , based on the total number of nest of all species occurring in such RG and weighted by the sampling area (Eq. 3). The sampling area employed in Eq. 3 is subedited to the RG (See Seifert 2017 for more details).
As a short example let us say that we are interested in calculating nest densities of M. rubra per grassland sites. Our estimations would be focused on the RG iii (common grassland species; Seifert 2017), and FRSD and pseudo-area would be based on Ssampling area (64 m 2 ). Thus, in an hypothetical community of five species and 11 nests resulting from a Seifert-plot with S= 3 nests (2 M. rubra), Q= 4 nests (1 M. rubra) and SI= 4 nests (0 M. rubra), the FRSD and pseudo-area will be 4.688 nests/100 m 2 and 2.347 m 2 respectively, and the integrated species-specific density (ISSD) for M. rubra will be 1.278 nests/100 m 2 . Finally, following the Seifert (2017) method, we used accumulation and extrapolation curves for sampling completeness assessment. This method aims to estimate the species number S found in a certain habitat as a function of sampling effort E (quantified by m 2 ), and used a natural logarithmic function (S = a Ln E + b) for constructing extrapolation curves. For a more details consult Seifert 2017).
The figure shows accumulation (blue line) and extrapolation curves (colored line) with 95% confidence interval (shaded area) for each grassland site. Sites with only one recorded species were excluded (G46, G48, G49 G50, and G201). Between 60% and 98% of the ant fauna were recorded per grassland site based on logarithmic extrapolation and minimum sampling effort E employed (460 m 2 ; red dotted line).

Box S2. Community structure descriptive results
Cluster analysis revealed a pattern of species composition within grassland sites related to management type, total nest density, and their most dense species or group of species ( Figure A2.1). The first division (k=2) generated one group of communities with high nest density in sites predominately managed as pasture and another group of communities with low density located in sites either managed as pasture or meadow ( Figure A2.1). In the first group, a second division (k=3) separated uneven communities with high M. scabrinodis density (cluster 1) from sites with intermediate densities of this species but relatively high densities of other species (clusters 3, 6; Figure A2.1). A third division (k=4) in the second group separated grassland sites with high nest densities of L. flavus (cluster 4) from sites with even communities of rather low nest density (cluster 2; Figure A2.1), with a further division (k=5) of this branch grouping sites where M. rubra was the most dense species (cluster 5). A fifth division (k=6) split grasslands sites with intermediate densities of M. scabrinodis (cluster 3) from sites with high densities of L. niger (cluster 6).
Internal cluster validation showed a decrease of the level of goodness and misplaced grassland sites with the increase of k clusters generation (Table A2.1). We considered k=6 as the most appropriated number of clusters with an overall S i = 0.38 and non-misplaced data points. Divisions with k > 7 led to S i = 0 within clusters suggesting that the algorithm does not succeed in finding any "natural" clustering (Rousseeuw 1987). Figure S2.1. Dendogram represents a hierarchical cluster analysis based on species density in 32 grasslands sites. Colorcoded clusters highlight the first group division, k values illustrate the number of cluster groups generated per division (stepwise), symbols at the cluster"s end show the final clustering-group in the analysis, and codes (leaf labels) at dendrogram"s bottom show the grassland site ID (see Figure 1). The bubble chart represents the species density matrix, arranged from high to low nest density per species. Total species density and RLE0,2 values are also provided for each grassland site. Evenness analysis was not performed in grassland sites with only one species.   Figure S2. Relationship between whole-community nest density and significant a) local and b) landscape environmental heterogeneity measures. Line and shaded area (square and error bar in the boxplot) show estimated effect and confidence interval (95%) according GLMs, dots represent observations per grassland color-coded by management type (pasture: lightfilled; meadow: dark-filled). Figure S3. Ant species responses to a) local and b) landscape environmental heterogeneity measures as standardized coefficients from multivariate SDMs (R 2 local = 0.23, R 2 landscape = 0.19). Size of coefficients can be interpreted as a measure of predictor importance. Color hue indicates positive (blue) or negative (red) species-predictor association, while shading indicates the magnitude of association. All predictors fitted in SDMs are shown.